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Detecting inference attacks involving sensor data in a multi-database context: Issues & challenges

Abstract : Nowadays applications produce and manage data of individual among which some may be sensitive and must be protected. Moreover, with the advent of smart applications, sensor data are produced by IoT devices in a huge quantity and sent to servers in the vicinity to be stored and processed. Meanwhile, newly discovered inference channels involving sensor data gives insights on personal data and raises new threats on individuals privacy. They escape the vigilance of traditional inference detection systems devoted to protecting personal data stored locally in a database. In this paper, we motivate the need of a distributed inference detection system acting in a general multi-database context and we highlight the issues that such a system would face.
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https://hal.archives-ouvertes.fr/hal-03623026
Contributor : Paul Lachat Connect in order to contact the contributor
Submitted on : Friday, September 23, 2022 - 11:20:16 AM
Last modification on : Wednesday, October 26, 2022 - 4:34:45 PM

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Internet Technology Letters - ...
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Paul Lachat, Nadia Bennani, Veronika Rehn-Sonigo, Lionel Brunie, Harald Kosch. Detecting inference attacks involving sensor data in a multi-database context: Issues & challenges. Internet Technology Letters, 2022, ⟨10.1002/itl2.387⟩. ⟨hal-03623026v2⟩

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